Visual Reconstruction and Localization-Based Robust Robotic 6-DoF Grasping in the Wild
The intelligent grasping expects that the manipulator has the ability to grasp objects with high degree of freedom in a wild (unstructured) environment. Due to low perception ability in handing targets and environments, most industrial robots are limited to top-down 4-DoF grasping. In this work, we...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9427547/ |
| _version_ | 1818361723584774144 |
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| author | Ji Liang Jiguang Zhang Bingbing Pan Shibiao Xu Guangheng Zhao Ge Yu Xiaopeng Zhang |
| author_facet | Ji Liang Jiguang Zhang Bingbing Pan Shibiao Xu Guangheng Zhao Ge Yu Xiaopeng Zhang |
| author_sort | Ji Liang |
| collection | DOAJ |
| description | The intelligent grasping expects that the manipulator has the ability to grasp objects with high degree of freedom in a wild (unstructured) environment. Due to low perception ability in handing targets and environments, most industrial robots are limited to top-down 4-DoF grasping. In this work, we propose a novel low-cost coarse to fine robotic grasping framework. First, we design a global localization based environment perception method, which enables the manipulator to roughly and automatically locate work space. Then, constrained by the above initial localization, a 3D point cloud reconstruction based 6-DoF pose estimation method is proposed for the manipulator further fine locating grasping target. Finally, our framework realizes full function of visual 6DoF robotic grasping, which includes two different visual servoing and grasp planning strategies for different objects grasping. Meanwhile, it also can integrate various state-of-arts 6DoF pose estimation algorithms to facilitate various practical grasping applications or researches. Experimental results show that our method achieves autonomous robotic grasping with high degree of freedom in an unknown environment. Especially for objects with occlusion, singular shape or small scale, our method can still maintain robust grasping. |
| first_indexed | 2024-12-13T21:21:13Z |
| format | Article |
| id | doaj.art-c7340e04f55b491281c927eff74453cf |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T21:21:13Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-c7340e04f55b491281c927eff74453cf2022-12-21T23:31:07ZengIEEEIEEE Access2169-35362021-01-019724517246410.1109/ACCESS.2021.30792459427547Visual Reconstruction and Localization-Based Robust Robotic 6-DoF Grasping in the WildJi Liang0Jiguang Zhang1https://orcid.org/0000-0002-8212-1361Bingbing Pan2Shibiao Xu3https://orcid.org/0000-0003-4037-9900Guangheng Zhao4Ge Yu5Xiaopeng Zhang6https://orcid.org/0000-0002-0092-6474Key Laboratory of Space Utilization, Technology and Engineering Center for space Utilization, Chinese Academy of Sciences, Beijing, ChinaNational Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, ChinaTechnology and Engineering Center for Space Utilization, University of Chinese Academy of Sciences, Beijing, ChinaTechnology and Engineering Center for Space Utilization, University of Chinese Academy of Sciences, Beijing, ChinaTechnology and Engineering Center for Space Utilization, University of Chinese Academy of Sciences, Beijing, ChinaKey Laboratory of Space Utilization, Technology and Engineering Center for space Utilization, Chinese Academy of Sciences, Beijing, ChinaTechnology and Engineering Center for Space Utilization, University of Chinese Academy of Sciences, Beijing, ChinaThe intelligent grasping expects that the manipulator has the ability to grasp objects with high degree of freedom in a wild (unstructured) environment. Due to low perception ability in handing targets and environments, most industrial robots are limited to top-down 4-DoF grasping. In this work, we propose a novel low-cost coarse to fine robotic grasping framework. First, we design a global localization based environment perception method, which enables the manipulator to roughly and automatically locate work space. Then, constrained by the above initial localization, a 3D point cloud reconstruction based 6-DoF pose estimation method is proposed for the manipulator further fine locating grasping target. Finally, our framework realizes full function of visual 6DoF robotic grasping, which includes two different visual servoing and grasp planning strategies for different objects grasping. Meanwhile, it also can integrate various state-of-arts 6DoF pose estimation algorithms to facilitate various practical grasping applications or researches. Experimental results show that our method achieves autonomous robotic grasping with high degree of freedom in an unknown environment. Especially for objects with occlusion, singular shape or small scale, our method can still maintain robust grasping.https://ieeexplore.ieee.org/document/9427547/Robotic graspingmanipulator6-DoF pose estimationpoint cloud reconstruction |
| spellingShingle | Ji Liang Jiguang Zhang Bingbing Pan Shibiao Xu Guangheng Zhao Ge Yu Xiaopeng Zhang Visual Reconstruction and Localization-Based Robust Robotic 6-DoF Grasping in the Wild IEEE Access Robotic grasping manipulator 6-DoF pose estimation point cloud reconstruction |
| title | Visual Reconstruction and Localization-Based Robust Robotic 6-DoF Grasping in the Wild |
| title_full | Visual Reconstruction and Localization-Based Robust Robotic 6-DoF Grasping in the Wild |
| title_fullStr | Visual Reconstruction and Localization-Based Robust Robotic 6-DoF Grasping in the Wild |
| title_full_unstemmed | Visual Reconstruction and Localization-Based Robust Robotic 6-DoF Grasping in the Wild |
| title_short | Visual Reconstruction and Localization-Based Robust Robotic 6-DoF Grasping in the Wild |
| title_sort | visual reconstruction and localization based robust robotic 6 dof grasping in the wild |
| topic | Robotic grasping manipulator 6-DoF pose estimation point cloud reconstruction |
| url | https://ieeexplore.ieee.org/document/9427547/ |
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